Volume 2 Issue 5, May 2020

Volume 2 Issue 5

Predicting lung cancer survival.

Lung cancer has a low survival rate and non-small cell lung cancer (NSCLC) accounts for 85% of lung cancer diagnoses. Mukherjee et al. developed and validated LungNet, an image-based deep learning approach trained on cohorts from various clinical centres, for the pre-diction of overall survival of patients with NSCLC. Their model outputs a risk score that can be used to stratify patients into high- and low-risk categories with regard to overall survival. Using transfer learning, the authors further showed that their survival model can be used for classifying benign versus malignant nodules.

See Mukherjee et al.

Image: Pritam Mukherjee, Stanford University. Cover Design: Karen Moore.

Editorial

  • Editorial |

    Scientists have been getting concerned about the carbon footprint of international meetings and have been asking whether travelling to conferences is the best use of their time and funds. 2020 is turning out to be the year that many organizers decide to go virtual — and this was before COVID-19.

News & Views

  • News & Views |

    To deploy robot swarms in our daily lives, they need to be resilient to malfunctioning errors and protected against malicious attacks. Blockchain technology could provide an essential level of protection.

    • Andreagiovanni Reina

Comment & Opinion

  • Comment |

    The Catholic Church is challenged by scientific and technological innovation but can help to integrate multiple voices in the ongoing dialogue regarding AI and machine ethics. In this context, a multidisciplinary working group brought together by the Church reflected on roboethics, explored the themes of embodiment, agency and intelligence.

    • Edoardo Sinibaldi
    • , Chris Gastmans
    • , Miguel Yáñez
    • , Richard M. Lerner
    • , László Kovács
    • , Carlo Casalone
    • , Renzo Pegoraro
    •  & Vincenzo Paglia

Research

  • Article |

    Current neural networks attempt to learn spatial and temporal information as a whole, limiting their ability to process complex video data. Pang et al. improve performance by introducing a network structure which learns to implicitly decouple complex spatial and temporal concepts.

    • Bo Pang
    • , Kaiwen Zha
    • , Hanwen Cao
    • , Jiajun Tang
    • , Minghui Yu
    •  & Cewu Lu
  • Article |

    While computerization and digitization of medicine have advanced substantially, management tools in healthcare have not yet benefited much from these developments due to the extreme complexity and variability of healthcare operations. The ability of machine learning algorithms to build strong models from a large number of weakly predictive features, and to identify key factors in complex feature sets, is tested in operational problems involving hospital datasets on workflow and patient waiting time.

    • Oleg S. Pianykh
    • , Steven Guitron
    • , Darren Parke
    • , Chengzhao Zhang
    • , Pari Pandharipande
    • , James Brink
    •  & Daniel Rosenthal
  • Article |

    Predicting overall survival for patients with confirmed non-small-cell lung cancer is an important issue in clinical practice. The authors developed and validated in four independent patient cohorts a shallow convolutional neural network that can predict the outcomes of individuals using pre-treatment CT images. The authors further show that the survival model can be used, via transfer learning, for classifying benign versus malignant nodules.

    • Pritam Mukherjee
    • , Mu Zhou
    • , Edward Lee
    • , Anne Schicht
    • , Yoganand Balagurunathan
    • , Sandy Napel
    • , Robert Gillies
    • , Simon Wong
    • , Alexander Thieme
    • , Ann Leung
    •  & Olivier Gevaert
  • Article |

    Early and accurate clinical assessment of disease severity in COVID-19 patients is essential for planning the allocation of scarce hospital resources. An explainable machine learning tool trained on blood sample data from 485 patients from Wuhan selected three biomarkers for predicting mortality of individual patients with high accuracy.

    • Li Yan
    • , Hai-Tao Zhang
    • , Jorge Goncalves
    • , Yang Xiao
    • , Maolin Wang
    • , Yuqi Guo
    • , Chuan Sun
    • , Xiuchuan Tang
    • , Liang Jing
    • , Mingyang Zhang
    • , Xiang Huang
    • , Ying Xiao
    • , Haosen Cao
    • , Yanyan Chen
    • , Tongxin Ren
    • , Fang Wang
    • , Yaru Xiao
    • , Sufang Huang
    • , Xi Tan
    • , Niannian Huang
    • , Bo Jiao
    • , Cheng Cheng
    • , Yong Zhang
    • , Ailin Luo
    • , Laurent Mombaerts
    • , Junyang Jin
    • , Zhiguo Cao
    • , Shusheng Li
    • , Hui Xu
    •  & Ye Yuan

    Collection:

Amendments & Corrections